ArticleContemporary clinical trials communications2020
A population health dietary intervention for African American adults with chronic kidney disease: The Fruit and Veggies for Kidney Health randomized study.
Article in Contemporary clinical trials communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Gaining Community Trust Enables Research Designed to Reduce the National Burden of Chronic Kidney Disease.Journal of immigrant and minority health · 2026Review
- Qualitative Perspectives from African American Adults with CKD on Community-Based Fruit and Vegetable Promotion.Clinical journal of the American Society of Nephrology : CJASN · 2025Article
- The Fruit and Veggies for Kidney Health Study: A Prospective Randomized Trial.Kidney medicine · 2023Article
- A Lifestyle Intervention to Delay Early Chronic Kidney Disease in African Americans With Diabetic Kidney Disease: Pre-Post Pilot Study.JMIR formative research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundChronic kidney disease (CKD) is commonly asymptomatic until its late stages, reduces life quality and length, is costly to manage, and is disproportionately prevalent in low-income, African American (AA) communities. Traditional health system strategies that engage only patients with symptomatic CKD limit opportunities to prevent progression to end stage kidney disease (ESKD) with the need for expensive kidney replacement therapy and to reduce risk for their major mortality cause, cardiovascular disease (CVD). Published studies show that giving fruits and vegetables (F&V) to AA with early-stage CKD along with preparation instructions slowed CKD progression. This effective, evidenced-based, and potentially scalable dietary intervention might be a component of a community-based strategy to prevent CKD progression.
designThis study supported by NIH grant (R21DK113440) will test the feasibility of an innovative screening strategy conducted at community-based institutions in low-income AA communities and the ability to intervene in individuals identified to have CKD and increased CVD risk with F&V, with or without preparation instructions.
objectivesThe study will prospectively compare changes in urine indices predictive of CKD progression and CVD in participants receiving, compared to those not receiving, preparation instructions along with F&V, six months after the intervention. DISCUSSION: Addressing the challenge of increasing progression of early to more advanced stages of CKD with its increased CVD risk requires development of effective strategies to screen, identify, and intervene with individuals found to have CKD with effective, comparatively inexpensive, community-based, and scalable strategies to prevent CKD progression, particularly in low-income, AA communities.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.